Combating information overload with different data sources [Q&A]

The majority of teams today are contending with too much data which means they struggle to generate meaningful insights from their information, and can become overwhelmed by the sheer volume.

We spoke to CallMiner CMO Eric Williamson who believes sourcing customer feedback from different sources might help solve the problem.

BN: In what ways has the volume of customer data evolved in recent years?

EW: Today's organizations are interacting with their customers on more channels than ever before -- from phone, email, and SMS to social media, self-service applications, and more. This has created an influx of customer data. This data, if you have the right technology and tools, can tell you how customers feel about your products, services, and brand, indicating things like churn risk, loyalty, and satisfaction. But just because organizations have more data than ever doesn’t necessarily mean that they’re able to uncover and use these types of insights for impactful business change.

A recent survey from Salesforce confirms that many teams are contending with too much data, with 33 percent of business leaders saying they can't generate meaningful insights from their data, and 30 percent reporting they are overwhelmed by the sheer volume.

CallMiner's own data from our annual CX Landscape Report validates this as well. Forty-seven percent of global customer experience (CX) and contact center leaders strongly agreed that digital transformation has unlocked a wealth of data for CX teams -- but more than two-thirds (68 percent) said this data is often not harnessed to their organization’s best advantage.

We're now in an age where having enough customer data isn't the issue -- instead, more organizations are struggling to use that data effectively to drive meaningful change and improvements in their business.

BN: Not all customer data and feedback is created equal. Why is unsolicited feedback so important for organizations to capture? How can organizations combine this data with solicited feedback?

EW: In the CX and contact center industries, we usually put customer data and feedback into two categories -- solicited and unsolicited. Solicited feedback is collected when organizations directly ask customers for input, usually through mechanisms like surveys or reviews. This feedback is certainly valuable, but as consumers ourselves, it's not hard to understand why this type of feedback is limited and polarizing. Customer surveys, for example, often have low response rates or they capture responses on either end of the extreme emotional spectrum.

CallMiner's research indicates that the majority of organizations today (71 percent) are still collecting mostly solicited feedback.

Unsolicited feedback, on the other hand, is feedback that customers are telling you without being asked. These unsolicited insights are contained within contact center interactions (voice and text), social media posts, and more. When organizations collect and combine both solicited and unsolicited data sources, they get a more complete view of the voice of the customer (VoC), CX, agent performance, and more -- all of which can be used to make better business decisions and drive better customer outcomes.

BN: How can AI-driven conversation intelligence solutions alleviate the burden of gathering and assessing unsolicited feedback to prevent teams from feeling overwhelmed?

EW: One of the issues with customer data overload is that it's impossible for organizations to analyze a high volume of data manually at scale. AI-driven technology, such as conversation intelligence solutions, makes it possible to capture and analyze 100 percent of omnichannel customer conversations. Within the contact center, for example, supervisors are only able to review a small percentage of agent interactions manually. This makes it incredibly hard to truly understand trends in KPIs like agent performance, customer sentiment, and more. AI and machine learning (ML) allow organizations to go beyond what humans can execute manually and derive valuable insights for business improvements.

Further, by eliminating repetitive, manual tasks, AI helps employees be more productive and empowers them to focus on actions that improve CX -- whether that’s analysts who can spend more time uncovering important customer trends, supervisors who can spend more time coaching agents, or agents helping to handle (and solve) more complex customer issues.

By automating the collection and analysis of CX data, organizations gain a deeper understanding of what their customers want, need, and expect, alleviating data overload and enabling teams to drive meaningful change across their organizations.

BN: How can decision-makers leverage customer data to drive meaningful business improvements?

EW: While there is still a gap in how organizations use CX data and insights to better inform business decisions -- almost three-quarters (72 percent) of organizations admit to not fully using their CX data to the best of their ability -- there are some getting it right.

I believe that there are a few common characteristics of these organizations and their business leaders. First, they are making smart technology investments. They understand that while their employees are invaluable, it's impossible to analyze the sheer amount of data they have at scale. Technology, like AI, takes away a lot of the heavy lifting of manual tasks and lets humans focus on more impactful tasks, like understanding customer trends.

Second, they thrive on cross-collaboration and data sharing. At many organizations today, contact center and CX departments are separate. Yet, the contact (or customer service) center is often the frontline of CX. Those interactions, more often than not, strengthen or weaken things like customer loyalty and satisfaction. Without cross-collaboration between these two departments, contact centers lack context for how their conversations are impacting overall CX and vice versa.

Finally, they understand that customer insights can impact and benefit every department, enterprise-wide. They use customer insights to improve decision making in marketing (such as improving campaign effectiveness), in product (such as identifying product issues or making smarter product development decisions), in sales (such as identifying language that results in deals closed), and more.

BN: OpenAI recently launched a new AI model and desktop version of ChatGPT. With wider adoption of AI among consumers, how do you anticipate generative AI will impact customer service and data as more organizations adopt the technology?

EW: Generative AI continues to drive innovation across industries -- and for good reason. It's one of the most transformative technological innovations of our time, with the potential to upend entire industries and how we work.

Within customer service specifically, generative AI will make organizations faster, smarter, and better. As a result, customers will benefit from improved products, services, and experiences. The key will be effectively balancing AI velocity and agility with responsibility and security.

Customer service organizations that get generative AI 'right' will be the ones that focus on what business goals it can help them achieve and apply generative AI to workflows that directly address those goals versus adopting generative AI for the sake of AI.

When organizations strategically orient around AI -- generative or otherwise -- they will not only effectively manage costs (which is a critical consideration), but they will also positively impact customer and business outcomes, driving tangible ROI from their investments.

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